CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Mean Shift, Mode Seeking, and Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple Object Tracking with Kernel Particle Filter
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Using Interval Particle Filtering for Marker Less 3D Human Motion Capture
ICTAI '05 Proceedings of the 17th IEEE International Conference on Tools with Artificial Intelligence
Kernel Particle Filter for Real-Time 3D Body Tracking in Monocular Color Images
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Shape and motion driven particle filtering for human body tracking
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 3 (ICME '03) - Volume 03
Dynamic kernel-based progressive particle filter for 3d human motion tracking
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part II
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The work proposes a model-based 3D human motion tracking algorithm, Progressive particle filter, with a single unconstrained camera. Particle filter is an useful algorithm for human motion tracking, but it suffers from the degeneracy problem and huge computation. The study improves the sampling efficiency by integrating the mean shift trackers into each particle toward each local maximum for raising the accuracy. Besides, we also combine the hierarchical searching approach to decompose the high dimensional space into three low dimensional spaces for reducing the computational cost. Experimental results show the proposed algorithm can successfully reduce the computational cost and track more accuracy than classical particle filter.